


from __future__ import annotations

from math import pi, sqrt






def 				__lowercase  (					__lowerCAmelCase				:							List[str]   ,       __lowerCAmelCase				:							str  ):

							if inductance <= 0:
														raise ValueError('Inductance cannot be 0 or negative'  )

							elif capacitance <= 0:
														raise ValueError('Capacitance cannot be 0 or negative'  )

							else:
														return (
														    "Resonant frequency",
														    float(1 / (2 * pi * (sqrt(inductance * capacitance  )))  ),
														)


if __name__ == "__main__":
						import doctest

						doctest.testmod()






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCamelCase_    :			Union[str, Any]    =	{
    """configuration_tapas""": ["""TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TapasConfig"""],
    """tokenization_tapas""": ["""TapasTokenizer"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_    :			List[Any]    =	[
							    """TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TapasForMaskedLM""",
							    """TapasForQuestionAnswering""",
							    """TapasForSequenceClassification""",
							    """TapasModel""",
							    """TapasPreTrainedModel""",
							    """load_tf_weights_in_tapas""",
							]
try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_    :			Optional[Any]    =	[
							    """TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFTapasForMaskedLM""",
							    """TFTapasForQuestionAnswering""",
							    """TFTapasForSequenceClassification""",
							    """TFTapasModel""",
							    """TFTapasPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
							from .tokenization_tapas import TapasTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tapas import (
														    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TapasForMaskedLM,
														    TapasForQuestionAnswering,
														    TapasForSequenceClassification,
														    TapasModel,
														    TapasPreTrainedModel,
														    load_tf_weights_in_tapas,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_tapas import (
														    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFTapasForMaskedLM,
														    TFTapasForQuestionAnswering,
														    TFTapasForSequenceClassification,
														    TFTapasModel,
														    TFTapasPreTrainedModel,
														)


else:
							import sys

							lowerCamelCase_    :			Any    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






